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  • Open Access

    ARTICLE

    Preliminary Study on the Treatment Efficiency of Pasteurized Lime Thermal Alkaline Hydrolysis for Excess Activated Sludge and Reduction of Tetracycline Resistance Genes

    Maoxia Chen1,2,*, Qixuan Zhou1, Jiayue Zhang1, Jiaoyang Li1, Wei Zhang1, Huan Liu1

    Journal of Renewable Materials, Vol.11, No.10, pp. 3711-3723, 2023, DOI:10.32604/jrm.2023.027826

    Abstract Thermal alkaline hydrolysis is a common pretreatment method for the utilization of excess activated sludge (EAS). Owing to strict environment laws and need for better energy utilization, new methods were developed in this study to improve the efficiency of pretreatment method. Direct thermal hydrolysis (TH), pasteurized thermal hydrolysis (PTH), and alkaline pasteurized thermal hydrolysis (PTH + CaO and PTH + NaOH) methods were used to treat EAS. Each method was compared and analyzed in terms of dissolution in ammonium nitrogen (NH4 + -N) and soluble COD (SCOD) in EAS. Furthermore, the removal of tetracycline resistance genes… More >

  • Open Access

    ARTICLE

    Dimensionality Reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification

    S. Srinivasan, K. Rajakumar*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2481-2496, 2023, DOI:10.32604/csse.2023.040817

    Abstract

    The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors. The high correlation between these features and the noises greatly affects the classification performances. To overcome this, dimensionality reduction techniques are widely used. Traditional image processing applications recently propose numerous deep learning models. However, in hyperspectral image classification, the features of deep learning models are less explored. Thus, for efficient hyperspectral image classification, a depth-wise convolutional neural network is presented in this research work. To handle the dimensionality issue in the classification process, an optimized self-organized map model is employed

    More >

  • Open Access

    REVIEW

    Deep Learning Applied to Computational Mechanics: A Comprehensive Review, State of the Art, and the Classics

    Loc Vu-Quoc1,*, Alexander Humer2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130

    Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting… More >

  • Open Access

    ARTICLE

    Experimental and Numerical Analysis of Oil-Water Flow with Drag Reducing Polymers in Horizontal Pipes

    Amer A. Abdulrahman1, Bashar J. Kadhim1, Zainab Y. Shnain1, Hassan Sh. Majidi2, Asawer A. Alwaiti1,*, Farooq Al-Sheikh1, Adnan A. AbdulRazak1, Mohammed Shorbaz1, Mazin J. Shibeeb3

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2579-2595, 2023, DOI:10.32604/fdmp.2023.027454

    Abstract The well-known frictional effect related to liquid-liquid two-phase flow in pipelines can be reduced using drag-reducing additives. In this study, such an effect has been investigated experimentally using a mixture of oil and water. Moreover, numerical simulations have been carried out using the COMSOL simulation software. The measurements were taken in a horizontal pipe with the length and diameter equal to 3 and 0.125 m, respectively. Moreover, Polyethylene oxide with 150 ppm was exploited to reduce the drag effect while considering different water-to-oil fractions (0.3, 0.4, 0.5, and 0.7) and a constant total flow velocity More >

  • Open Access

    ARTICLE

    An Optimized Implementation of a Novel Nonlinear Filter for Color Image Restoration

    Turki M. Alanazi*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1553-1568, 2023, DOI:10.32604/iasc.2023.039686

    Abstract Image processing is becoming more popular because images are being used increasingly in medical diagnosis, biometric monitoring, and character recognition. But these images are frequently contaminated with noise, which can corrupt subsequent image processing stages. Therefore, in this paper, we propose a novel nonlinear filter for removing “salt and pepper” impulsive noise from a complex color image. The new filter is called the Modified Vector Directional Filter (MVDF). The suggested method is based on the traditional Vector Directional Filter (VDF). However, before the candidate pixel is processed by the VDF, the MVDF employs a threshold… More >

  • Open Access

    ARTICLE

    Two-Layer Information Granulation: Mapping-Equivalence Neighborhood Rough Set and Its Attribute Reduction

    Changshun Liu1, Yan Liu1, Jingjing Song1,*, Taihua Xu1,2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2059-2075, 2023, DOI:10.32604/iasc.2023.039592

    Abstract Attribute reduction, as one of the essential applications of the rough set, has attracted extensive attention from scholars. Information granulation is a key step of attribute reduction, and its efficiency has a significant impact on the overall efficiency of attribute reduction. The information granulation of the existing neighborhood rough set models is usually a single layer, and the construction of each information granule needs to search all the samples in the universe, which is inefficient. To fill such gap, a new neighborhood rough set model is proposed, which aims to improve the efficiency of attribute… More >

  • Open Access

    ARTICLE

    Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

    Hassan Shokouhandeh1, Mehrdad Ahmadi Kamarposhti2,*, William Holderbaum3, Ilhami Colak4, Phatiphat Thounthong5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 809-822, 2023, DOI:10.32604/csse.2023.035827

    Abstract The widespread penetration of distributed energy sources and the use of load response programs, especially in a microgrid, have caused many power system issues, such as control and operation of these networks, to be affected. The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid. In this paper, the optimum operation of distributed generation resources and heat and power storage in a microgrid, was performed based on real-time pricing through the proposed gray wolf optimization (GWO) algorithm to reduce the… More >

  • Open Access

    ARTICLE

    Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

    Fang Yu1, Wenbin Bi2, Ning Cao3,4,*, Hongjun Li1, Russell Higgs5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1-17, 2023, DOI:10.32604/csse.2023.018349

    Abstract In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation, at the smart contract level of the blockchain, a customer churn prediction framework based on situational awareness and integrating customer attributes, the impact of project hotspots on customer interests, and customer satisfaction with the project has been built. This framework introduces the background factors in the financial customer environment, and further discusses the relationship between customers, the background of customers and the characteristics of pre-lost customers. The improved Singular… More >

  • Open Access

    ARTICLE

    Distributed Robust Optimal Dispatch for the Microgrid Considering Output Correlation between Wind and Photovoltaic

    Ming Li1,*, Cairen Furifu1, Chengyang Ge2, Yunping Zheng1, Shunfu Lin2, Ronghui Liu2

    Energy Engineering, Vol.120, No.8, pp. 1775-1801, 2023, DOI:10.32604/ee.2023.027215

    Abstract As an effective carrier of integrated clean energy, the microgrid has attracted wide attention. The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids. This paper proposes an optimization scheme based on the distributionally robust optimization (DRO) model for a microgrid considering solar-wind correlation. Firstly, scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function; then the generated scenario results are reduced by K-means clustering; finally, the probability confidence interval More >

  • Open Access

    ARTICLE

    Fusing Supervised and Unsupervised Measures for Attribute Reduction

    Tianshun Xing, Jianjun Chen*, Taihua Xu, Yan Fan

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 561-581, 2023, DOI:10.32604/iasc.2023.037874

    Abstract It is well-known that attribute reduction is a crucial action of rough set. The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations. Normally, the learning performance of attributes in derived reduct is much more crucial. Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct, those measures may have a direct impact on the performance of selected attributes in reduct. However, most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective, which… More >

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